Abstract:
Liver diseases are among the leading causes of death worldwide. The most useful approach for
controlling the growth of disease to reach at severe condition is to treat these diseases at the
early stages. Early treatment requires early diagnosis, which needs an accurate and reliable
diagnostic procedure. The aim of this study is to develop a computer-aided diagnostic (CAD)
system to achieve aforementioned objective. Computed tomography (CT) is one of the most
common and robust imaging techniques for the detection of liver lesions such as hepatocellular
carcinoma. Although in recent years the quality of CT images has been significantly improved,
however in some cases image interpretation by human beings is often limited. So we tried to
develop an automated system to detect and classify liver anomalies using CT images. Region of
interest from CT images was segmented using Active contours (snakes) algorithm [2] and
segmented image is used to extract statistical features by co occurrence matrix [4]. To facilitate
the classification of hepatic lesions, Support Vector Machine [10] and neural networks are used.
The results show that it is possible to automatically identify patients with liver lesions like
Hemengioma, Hepatoma or Cirhhosis based on texture features and that the machine
performance is marvelous and can assist human experts
